MIMO processing is now being used in a wide range of wireless communication systems. The adoption of MIMO techniques has been driven by the need to provide ever increasing data throughput to ever increasing numbers of users whilst simultaneously improving the system spectral efficiency. MIMO processing enables improvements in data throughput and spectral efficiency by making it possible for the transmitter, often referred to as base-station or node-B in cellular systems, to simultaneously transfer multiple information streams to the user. At the receiver, different techniques can be used to retrieve the transmitted information. Linear Minimum Mean Square Error (LMMSE) processing is a commonly used technique. The LMMSE receiver provides a reasonably good estimation performance with a fairly low complexity cost. Nevertheless, the LMMSE approach is sub-optimum and it is often desirable to use solutions with a performance closer to that of the MLD receiver in order to maximise the efficiency of wireless communication systems using MIMO processing. Unfortunately, predicting the performance of the MLD receiver, or that of techniques with a performance close to that of the MLD receiver, is a very challenging task when MIMO channels are considered.
Link level performance prediction has traditionally been used for two main applications, namely system-level simulations and generation of channel quality measurement feedback for link adaptation. Pre-coding based MIMO techniques used in wireless communication standards such as Wi-Fi (IEEE 802.16m-2011, “Amendment to IEEE Standard for Local and metropolitan area networks, Part 16: Air Interface for Broadband Wireless Access Systems—Advanced Air Interface”), 3GPP WCDMA (3GPP TS 25.211, “Physical Channels and Mapping of Transport Channels onto Physical Channels (FDD) (Release 7)”, V7.10.0) and 3GPP LTE (3GPP TS 36.211, “Evolved Universal Terrestrial Radio Access (E-UTRA); Physical Channels and Modulation (Release 8)”, V8.9.0) require the link level performance predicted by the receiver to be fed-back to the transmitter in the form of a Channel Quality Indicator (CQI) metric (3GPP TS 36.213, “Evolved Universal Terrestrial Radio Access (E-UTRA); Physical layer procedures (Release 8)”, V8.8.0). The CQI information is then used by the transmitter to adapt the transmission format to the propagation conditions. The overall objective of such a link adaptation mechanism is to select the transmission format with the best efficiency and with a high probability of the receiver correctly retrieving the information. The efficiency of different transmission formats is usually measured as spectral efficiency, but other metrics, such as power efficiency, may also be considered. The CQI will usually include information on the modulation and coding scheme that can be used by the transmitter in order to achieve a target receiver detection performance. The target receiver performance can be expressed through a number of different metrics such as Bit Error Rate (BER), Block Error Rate (BLER), and throughput, to name a few.
The aim of the link level performance prediction techniques presented in this disclosure is to predict or estimate the performance of the MLD receiver in a given MIMO channel through a set of Signal to Noise Ratios (SNR) values. Each stream in the MIMO system is associated with a different SNR which corresponds to the SNR at which the demodulation performance of the receiver would be achieved in an Additive White Gaussian Noise (AWGN) channel. These SNR values, also referred to as effective SNR values, can then be used to estimate the BER and/or BLER performance of the receiver in the given MIMO channel using mapping functions derived from the AWGN channel performance. These mapping functions are specific to the technique used by the receiver to retrieve the transmitted information. They can be generated through link-level simulations or can be derived theoretically and typically need to be matched to characteristics of the modulation and coding scheme used by the transmitter. The receiver can therefore use the effective SNR values to generate the CQI feedback by identifying at least one transmission format which will allow the target performance to be achieved. In wireless communication systems using Orthogonal Frequency Division Multiplexing (OFDM), as is the case for example in 3GPP LTE, multiple SNR values may be generated for the different sub-carriers. These multiple SNR values then typically need to be combined into a single SNR value from which it is possible to infer the receiver performance across the entire transmitted signal bandwidth.
Hence, the problem addressed in this disclosure can be expressed as that of predicting the performance of the receiver in a given MIMO channel. This is relatively simple to achieve when the receiver implements the LMMSE equalizer. However, the problem of predicting the performance of the MLD receiver in a MIMO channel is a more complex one. The joint decoding of the multiple symbols transmitted by the different streams makes it difficult to come up with a simple metric that accurately predicts the decoding performance across all the transmitted streams. Moreover, it is highly desirable to limit the computational complexity of the performance prediction technique in order to enable real-time processing in receivers where stringent cost and power limitations may apply. When applied to the generation of the CQI values used to support link adaptation, the performance prediction techniques also need to be able to achieve good accuracy over the whole range of transmission formats supported by the transmitter. Different transmission formats will typically use different modulation schemes as well as different error correction coding rates and the performance prediction accuracy needs to be maintained across all the formats.
Several attempts have been made previously to derive mapping functions for the generation of effective SNR values in MIMO channels. These attempts have usually been derived by extending to the MIMO channel approaches originally designed for single transmit antenna systems (see for example R. Yaniv, et al., “CINR Measurement using the EESM method”, IEEE C802.16e-05/141r1, March 2005; K. Sayana, J. Zhuang, K. Stewart, Motorola Inc, “Link Performance Prediction based on Mean Mutual Information per Bit (MMIB) of the LLR Channel [S]”. May 2, 2007). Unfortunately these different techniques suffer from a number of limitations. First, these methods are not general since they assume that the same constellation is used on all the streams (H. Liu, L. Cai, H. Yang, D. Li, EESM Based Link Error Prediction for Adaptive MIMO-OFDM System, In Proc. Vehicular Technology Conference, 2007. VTC2007-Spring. IEEE 65th 22-25 Apr. 2007, pp. 559-563; K. Sayana, J. Zhuang, K. Stewart. “Short Term Link Performance Modeling for ML Receivers with Mutual Information per Bit Metrics”, In Proc. GLOBECOM, 2008, pp. 4313-4318). Hence, these techniques cannot be applied to systems where different modulation schemes are used for the different streams. Moreover, the lack of a solid theoretical foundation for these different techniques leads to the need for complex look-up tables derived from link-level performance simulations.
Both the Mean Mutual Information per Bit (MMIB) and the Received Block Information Rate (RBIR) approaches require complex eigenvector decompositions in order to estimate the performance of the MLD receiver in MIMO channels and only achieve limited performance accuracy. The alternative Exponential Effective SNR Mapping (EESM) method has also been shown to lead to large inaccuracies when used to predict the performance of the MLD receiver in MIMO channels. When applied to MIMO channels, both EESM and MMIB techniques assume joint encoding of the MIMO streams and therefore predict the MLD performance as a single metric common to the different streams. Hence, these techniques cannot be used when the different streams are generated and encoded independently. An extension to the RBIR method, referred to as Extended RBIR (ERBIR), has recently been proposed for MIMO channels (J. Zhang, H. Zheng, Z. Tan, Y. Chen, L. Xiong, “Link Evaluation for MIMO-OFDM System with ML Detection”, In Proc. IEEE International Conference on Communication (ICC) 2010). This method is still limited to configurations where identical modulation schemes are used for the different streams and hence isn't suitable for a number of applications. For example, the 3GPP LTE standard supports the transmission of streams with different modulation schemes and hence the ERBIR approach couldn't be applied for the CQI generation in cellular communication systems following the 3GPP LTE standard. Moreover, whilst the ERBIR approach shows good prediction accuracy, it suffers from a very high computational complexity. This is due to the need for the calculation of the exact average mutual information in the form of integral formulas that are channel and constellation dependent.
Finally, the different prior-art techniques usually assume frequency-domain multiplexing and rely on averaging channel realizations in the frequency domain to achieve a good prediction performance. Hence, the accuracy of these approaches is low in propagation channels with limited frequency diversity.
The invention described herein provides techniques for the prediction of the MLD receiver performance in MIMO channels. The techniques presented in this disclosure achieve good accuracy with a limited complexity. These methods calculate an effective SNR which represents the SNR in the AWGN channel which would lead to the same demodulation performance as that experienced in the MIMO channel. The methods presented in this document can be used in systems where different modulation schemes are transmitted on the multiple MIMO streams and do not make on any assumption on the error correction coding. The predicted receiver performance can be used to generate link adaptation feedback as well as to adapt the receiver processing to the characteristics of the propagation link.